The present invention relates in general to wireless communication systems and, more particularly, to using antenna array and signal processing techniques to increase downlink capacity and performance of wireless communication systems.
The next generation of wireless mobile communication systems will be required to provide a variety of services including digital voice, video and data in different transmission modes. These services will require higher data rates and higher received signal power levels, thus creating increased interference between users. In order to obtain high system capacity, the interference levels have to be reduced dramatically. Spatial division multiple access (SDMA), by which a plurality of antenna elements are equipped at the base station in order to receive and transmit data information from and to the desired user by using spatial diversities, has been proposed as an effective technique to achieve this.
The main operations in SDMA include uplink (from mobile station to base station) beamforming and downlink (from base station to mobile station) beamforming. Uplink beamforming consists of uplink beamforming weight generation and uplink signal demultiplexing. Downlink beamforming includes downlink beamforming weight generation and downlink signal multiplexing. Theoretically, in both links, the associated channel responses are of critical importance in order to generate corresponding beamforming weights.
Usually the antenna array is equipped at base station, not at mobile terminals due to size limitation. Uplink beamforming is easy for implementation since uplink channel responses (UCRs) can be directly measured. Therefore much attention has been paid to uplink capacity enhancement. However, it is also desirable to improve downlink capacity in order to improve the whole system capacity. Moreover, downlink capacity is even more important for the next generation mobile communication systems in which wireless internet, video-on-demand and multimedia services are to be required.
In wireless communications, two duplex modes can be used: time-division-duplex (TDD) and frequency-division-duplex (FDD). For TDD mode, uplink and downlink channel responses are equal if the dwelling time is short enough. Thus UCRs can be used as downlink channel responses (DCRs) in determining downlink beamforming weights. This approach, however, requires accurate synchronization between uplink and downlink time slots, otherwise, interference between uplink and downlink signals can be seriously large. For FDD mode, since uplink and downlink employ different carrier frequencies, uplink and downlink signals will not interfere with each other. Therefore, FDD duplex mode is adopt in most current wireless communication systems, and most probably will be used in the next generation systems.
In FDD systems, UCRs are different from DCRs since the RF propagation environment differs at the uplink and downlink carrier frequencies. Hence, using antenna array at the base station to improve downlink performance is usually a more difficult problem than the associated uplink one due to lack of direct measurement of downlink channel responses (DCRs). In U.S. Pat. No. 5,472,647, D. Gerlach and A. Paulraj proposed one conceptually simple method, called probing-feedback approach, to estimate DCRs. In this approach, probing signals are first sent to the mobile users from base station in order to measure the instantaneous downlink channel vectors (IDCVs), then the IDCVs are feedback to the base station to generate downlink beamforming weights using certain criterion. This approach, however, is only applicable in environment which varys very slow in time. In another U.S. Pat. No. 5,634,199, D. Gerlach and A. Paulraj proposed to feedback the stable downlink channel vectors (SDCVs) in order to reduce the feedback rate. Both methods seem to be not advisable since they require complete redesign of uplink and downlink protocols and signaling. Moreover, these methods may greatly reduce the transmission and spectrum efficiency.
Another kind of approach for estimating DCRs is based upon direction-of-arrival (DOA) information embedded in received uplink signals. In fact, since uplink and downlink signals travel through reflections and deflections due to same scatters surrounding the mobile and the base station, the DOAs of the uplink signals might be the only constant parameters which can be used for downlink beamforming.
DOA-based approaches employ the received uplink signals to compute the desired user""s DOAs first, then DCRs are estimated by constructing downlink steering vectors for given DOAs. In International Patent Application Publication No. WO 97/45968, xe2x80x9cMethod of and apparatus for interference rejection combining and downlink beamforming in a cellular radiocommunications systemxe2x80x9d, (12/97), Forssen et al proposed to compute the probability function with respect to different DOAs at which the desired signal may come from, and to choose the angle of incidence associated with the particular mobile station as the DOA value which maximizes the probability function. This technique, however, suffers from heavy computational burden in computing the probability function and searching the maximum point. In another International Patent Application Publication No. WO 96/22662, xe2x80x9cSpectrally efficient high capacity wireless communication systemsxe2x80x9d, (7/96), Barratt et al use subspace-based techniques (e.g., MUSIC and ESPRIT) to obtain high-resolution DOA estimates from the covariance matrix of the antenna outputs. It is well known that subspace-based algorithms require very complicated computations since they are involved in the computation of matrix inversion or singular value decomposition of complex matrices, and one or even more multidimensional nonlinear optimizations. On the other hand, accurate DOA estimates are not available in multipath cases since the number of multipath DOAs are usually greater than the number of antenna elements. This may limit the applicability of the DOA-based approaches for estimating DCRs.
In fact, from U.S. Pat. No. 5,634,199, it is the downlink channel covariance matrices (DCCMS) that determine the downlink beamforming weights. Similar conclusions were drawn and exploited by C. Farsakh and J. A. Nossek in paper, xe2x80x9cSpatial covariance based downlink beamforming in an SDMA mobile radio systemxe2x80x9d, IEEE Trans. Comms., vol.46, No.11, 1998, pp.1497-1506. However, besides probing-feedback approach, the above two literatures failed to provide any efficient technique to compute DCCMs for FDD systems. Although in paper, xe2x80x9cDownlink beamforming for spatially distributed sources in cellular mobile communicationsxe2x80x9d, Signal Processing, Vol.65, 1998, 181-197, Goldberg and Fonollosa proposed a method for estimating DCCMs. This technique, however, also suffers from heavy computational burden and there is room to further simplify the computation of DCCM so that it is easier for practical implementation. Yet, the approach proposed by Goldberg and Fonollosa cannot be applied to the cases in which receive and transmit antenna structures are different from each other.
As such, the first objective of the present invention is to develop a computationally efficient technique for generating DCCMs and SDCVs for FDD systems.
Once DCCMs or SDCVs are obtained, the work left is to design downlink beamforming weights using DCCMs or SDCVs. Traditional approach is to use SDCVs as the downlink weight vectors. This approach, called maximal ratio combining (MRC) approach, is equivalent to keeping the main beam of the downlink beam pattern toward the intended user. Since uplink usually employs minimum mean-square-error (MMSE) beamforming scheme, which is much better than MRC method, the traditional approach is not able to provide enough capacity to match its uplink counterpart. Another approach is proposed by F. Rashid-Farroki et al in paper, xe2x80x9cTransmit beamforming and power control for cellular wireless systems,xe2x80x9d IEEE Journal of Selected Areas in Communications, vol.16, No.8, October 1998, pp. 1437-1449. This approach generates downlink beamforming weights using joint uplink beamforming and power control technique in which total transmitted power is to be minimized. This approach, however, does not consider data rate information, and more seriously, no efficient technique is suggested to jointly solve FDD and weight generation problem.
The next generation systems will be required to provide wireless internet, video-on-demand and multimedia services, thus users sharing the same channel may request higher data rates and higher received signal powers. If each user""s main beam is simply directed to the desired user without considering the interference polluted to the other users, the quality of the low rate user spatially closed to stronger users may be so poor that even the minimum quality requirement cannot be satisfied. Thus how to design downlink beamforming weights such that maximum number of users with different data rate services can be supported within the same channel and same cell or sector while keeping satisfactory communication quality becomes the second objective of the present invention.
As mentioned earlier, in SDMA wireless communications, the main operations include uplink weight generation and downlink weight generation. Since uplink beamforming weights are useful information at hand, the third objective of the present invention is to develop methods for generating downlink beamforming weights by direct modifying uplink ones.
The present invention comprises a wireless communication system which integrates base station antenna array and signal processing techniques to improve downlink performance and capacity of wireless communications.
According to the present invention, an apparatus for communicating with a plurality of wireless users is provided which consists of uplink receive antenna array, uplink weight generator and uplink spatial demultiplexing and downlink weight generator, downlink spatial multiplexing and downlink transmit antenna array. Downlink beamforming weights can be derived from uplink channel covariance matrices (UCCMs), or uplink channel responses (UCRs), or uplink beamforming weights. Thus no feedback or intermediate step for estimating DOAs is required. Also, downlink transmit antenna array can be same as or different from uplink receive antenna array.
According to one aspect of the present invention, uplink receive antenna array acquires a plurality of combinations of signals transmitted from the mobile users, from which UCRs or UCCMs are estimated. Downlink channel covariance matrices (DCCMs) or downlink channel responses (DCRs) can then be derived from UCCM or UCRs together with certain frequency calibration processing.
Advantageously, DCCMs can be estimated from UCCMs via peak constraint method. Peak constraint method generates DCCM by keeping same peak positions of main beams for the beam patterns generated from the eigenvectors of UCCM and DCCM. This method links columnized DCCM vector with columnized UCCM vector through a linear multiplication with a frequency calibration (FC) matrix, which is only dependent on uplink and downlink carrier frequencies, receive and transmit antenna array structures of the system, and can be computed and stored in advance. Thus, peak constraint method is a simple while efficient technique for overcoming FDD problem.
Conveniently, SDCVs can be estimated from UCRs using peak constraint or null constraint methods. For peak constraint method, the principal eigenvector of the estimated DCCM is used as SDCV. For null constraint, SUCV is first estimated by computing the principal eigenvector of the UCCM, which is obtained from IUCVs via time-average approach, then SDCV is generated by keeping same null positions for the beam patterns generated from both SUCV and SDCV.
According to one aspect of the present invention, downlink beamforming weights can be generated from DCCMs or SDCVs using different approaches, such as iterative virtual power weighted (IVPW) approach, virtual power weighted (VPW) approach or spatial distribution weighted (SDW) approach. Downlink data rate information is exploited in designing downlink beamforming weights in order to maximize the system capacity.
According to one aspect of the present invention, downlink beamforming weights can be generated by direct modifying uplink beamforming weights.
Preferably, downlink beamforming weights can be implemented by using normal uplink beamforming weights together with null constraint method. The method is simple for implementation in terms of hardware and software complexities since uplink beamforming weights are already at hand.
Advantageously, downlink beamforming can be implemented by using leaky uplink beamforming weights together with certain frequency calibration processing, such as peak constraint transform. This method provides another choice for implementing downlink beamforming since in some cases leaky uplink beamforming scheme is already used in order to keep the uplink weight from converging to pathological solutions.
The basic properties and benefits of the present invention are summarized as follows:
1. The present invention provides a high flexibility in the sense that different kind of uplink information can be used, such as uplink channel covariance matrices, uplink channel responses and uplink beamforming weights.
2. The present invention is simple to implement. It does not require downlink channel feedback, thus eliminating the need for modifying uplink and downlink protocols, and not require demanding DOA estimation and its association.
3. The main concern complicating FDD system is the lack of downlink channel responses. The present invention provides two methods for solving this problem: peak constraint method and null constraint method.
4. The present invention takes care of possibly different receive and transmit antenna array structures, no matter if the systems are TDD or FDD.
5. The present invention provides different methods for generating downlink beamforming weights based upon different uplink information used. Downlink data rate information is also exploited in order to maximize system capacity. These methods can be applied in both TDD and FDD systems.